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Features Importance

Spearman Correlation of Models

Summary of 1_Baseline
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Baseline Classifier (Baseline)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
7.7 seconds
Metric details
|
score |
threshold |
| logloss |
0.628023 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.486486 |
0.283929 |
| accuracy |
0.321429 |
0.283929 |
| precision |
0.321429 |
0.283929 |
| recall |
1 |
0.283929 |
| mcc |
0 |
0.283929 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.628023 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.486486 |
0.283929 |
| accuracy |
0.321429 |
0.283929 |
| precision |
0.321429 |
0.283929 |
| recall |
1 |
0.283929 |
| mcc |
0 |
0.283929 |
Confusion matrix (at threshold=0.283929)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
38 |
| Labeled as 1 |
0 |
18 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 2_DecisionTree
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Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
56.9 seconds
Metric details
|
score |
threshold |
| logloss |
1.51356 |
nan |
| auc |
0.729532 |
nan |
| f1 |
0.684211 |
0.297472 |
| accuracy |
0.785714 |
0.297472 |
| precision |
0.714286 |
0.8125 |
| recall |
0.833333 |
0 |
| mcc |
0.524389 |
0.297472 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
1.51356 |
nan |
| auc |
0.729532 |
nan |
| f1 |
0.684211 |
0.297472 |
| accuracy |
0.785714 |
0.297472 |
| precision |
0.65 |
0.297472 |
| recall |
0.722222 |
0.297472 |
| mcc |
0.524389 |
0.297472 |
Confusion matrix (at threshold=0.297472)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
31 |
7 |
| Labeled as 1 |
5 |
13 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
Top-10 Worst decisions for class 0 (Fold 1)

Top-10 Best decisions for class 0 (Fold 1)

Top-10 Worst decisions for class 1 (Fold 1)

Top-10 Best decisions for class 1 (Fold 1)

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Summary of 3_Linear
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Logistic Regression (Linear)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
12.3 seconds
Metric details
|
score |
threshold |
| logloss |
0.361632 |
nan |
| auc |
0.903509 |
nan |
| f1 |
0.810811 |
0.364006 |
| accuracy |
0.875 |
0.364006 |
| precision |
1 |
0.757 |
| recall |
1 |
0.000133374 |
| mcc |
0.718164 |
0.364006 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.361632 |
nan |
| auc |
0.903509 |
nan |
| f1 |
0.810811 |
0.364006 |
| accuracy |
0.875 |
0.364006 |
| precision |
0.789474 |
0.364006 |
| recall |
0.833333 |
0.364006 |
| mcc |
0.718164 |
0.364006 |
Confusion matrix (at threshold=0.364006)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
34 |
4 |
| Labeled as 1 |
3 |
15 |
Learning curves

Coefficients
| feature |
Learner_1 |
| serum_creatinine |
0.459312 |
| age |
0.414471 |
| diabetes |
0.353401 |
| high_blood_pressure |
0.326843 |
| anaemia |
0.297311 |
| creatinine_phosphokinase |
0.159132 |
| smoking |
-0.0403247 |
| sex |
-0.159142 |
| ejection_fraction |
-0.478172 |
| platelets |
-0.520779 |
| serum_sodium |
-0.524161 |
| time |
-1.45222 |
| intercept |
-1.70399 |
Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
Top-10 Worst decisions for class 0 (Fold 1)

Top-10 Best decisions for class 0 (Fold 1)

Top-10 Worst decisions for class 1 (Fold 1)

Top-10 Best decisions for class 1 (Fold 1)

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Summary of 4_Default_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: logloss
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
12.9 seconds
Metric details
|
score |
threshold |
| logloss |
0.499665 |
nan |
| auc |
0.83114 |
nan |
| f1 |
0.723404 |
0.247828 |
| accuracy |
0.785714 |
0.444885 |
| precision |
0.666667 |
0.799324 |
| recall |
1 |
0.0924742 |
| mcc |
0.58757 |
0.247828 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.499665 |
nan |
| auc |
0.83114 |
nan |
| f1 |
0.684211 |
0.444885 |
| accuracy |
0.785714 |
0.444885 |
| precision |
0.65 |
0.444885 |
| recall |
0.722222 |
0.444885 |
| mcc |
0.524389 |
0.444885 |
Confusion matrix (at threshold=0.444885)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
31 |
7 |
| Labeled as 1 |
5 |
13 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
Top-10 Worst decisions for class 0 (Fold 1)

Top-10 Best decisions for class 0 (Fold 1)

Top-10 Worst decisions for class 1 (Fold 1)

Top-10 Best decisions for class 1 (Fold 1)

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Summary of 5_Default_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
4.5 seconds
Metric details
|
score |
threshold |
| logloss |
0.454204 |
nan |
| auc |
0.831871 |
nan |
| f1 |
0.709677 |
0.47667 |
| accuracy |
0.839286 |
0.47667 |
| precision |
0.888889 |
0.676714 |
| recall |
1 |
0.00514851 |
| mcc |
0.617774 |
0.47667 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.454204 |
nan |
| auc |
0.831871 |
nan |
| f1 |
0.709677 |
0.47667 |
| accuracy |
0.839286 |
0.47667 |
| precision |
0.846154 |
0.47667 |
| recall |
0.611111 |
0.47667 |
| mcc |
0.617774 |
0.47667 |
Confusion matrix (at threshold=0.47667)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
36 |
2 |
| Labeled as 1 |
7 |
11 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 6_Default_RandomForest
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Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: logloss
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
19.8 seconds
Metric details
|
score |
threshold |
| logloss |
0.350767 |
nan |
| auc |
0.912281 |
nan |
| f1 |
0.818182 |
0.288106 |
| accuracy |
0.857143 |
0.288106 |
| precision |
0.818182 |
0.723054 |
| recall |
1 |
0.0200628 |
| mcc |
0.739296 |
0.288106 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.350767 |
nan |
| auc |
0.912281 |
nan |
| f1 |
0.818182 |
0.288106 |
| accuracy |
0.857143 |
0.288106 |
| precision |
0.692308 |
0.288106 |
| recall |
1 |
0.288106 |
| mcc |
0.739296 |
0.288106 |
Confusion matrix (at threshold=0.288106)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
30 |
8 |
| Labeled as 1 |
0 |
18 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
Top-10 Worst decisions for class 0 (Fold 1)

Top-10 Best decisions for class 0 (Fold 1)

Top-10 Worst decisions for class 1 (Fold 1)

Top-10 Best decisions for class 1 (Fold 1)

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Summary of Ensemble
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Ensemble structure
| Model |
Weight |
| 3_Linear |
2 |
| 6_Default_RandomForest |
3 |
Metric details
|
score |
threshold |
| logloss |
0.328403 |
nan |
| auc |
0.932749 |
nan |
| f1 |
0.829268 |
0.357868 |
| accuracy |
0.875 |
0.357868 |
| precision |
0.909091 |
0.604225 |
| recall |
1 |
0.012091 |
| mcc |
0.746678 |
0.357868 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.328403 |
nan |
| auc |
0.932749 |
nan |
| f1 |
0.829268 |
0.357868 |
| accuracy |
0.875 |
0.357868 |
| precision |
0.73913 |
0.357868 |
| recall |
0.944444 |
0.357868 |
| mcc |
0.746678 |
0.357868 |
Confusion matrix (at threshold=0.357868)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
32 |
6 |
| Labeled as 1 |
1 |
17 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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